ED/SVP, Data Science Lead, Institutional Banking Group Data Chapter

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Company Overview

Our client is a leading financial institution committed to driving innovation and leveraging cutting-edge technology to transform its business operations. They are dedicated to building a data-driven organization that maximizes key data capabilities to achieve exponential growth.

Job Title: ED/SVP, Data Science Lead, Institutional Banking Group Data Chapter

Business Function

The Institutional Banking Group Data Chapter is committed to building a data driven organization and maximizing the key data capabilities to transform how IBG does business. Data impacts IBG’s businesses every day, and a culture of relentlessly using data and business intelligence will enable IBG to achieve exponential growth.

Key Responsibilities

As the Lead Data Scientist, you will manage a team of data scientists within IBG Data Chapter to deliver business impact by leveraging AI & Generative AI. You will lead complex data science projects that drive decision-making in IBG, in areas across revenue generation, risk management, and productivity etc.

• Oversee the team in the design, development and delivery of AI/ML and Generative AI strategies and capabilities to support IBG’s strategic priorities, in alignment with the bank’s AI industrialization agenda.
• Engage with senior management and business stakeholders to identify opportunities where data science can deliver value.
• Provide leadership to the team in collaborating with various stakeholders from business and technology throughout the analytics lifecycle, from requirements gathering, solution iteration, deployment, to ongoing enhancements.
• Ensure robust model governance, including performance monitoring, validation, and re-calibration, in line with governance standards and best practices.
• Drive productivity and impact within the team by optimizing ways of working, by e.g. prioritization, building of reusable assets, leveraging Generative AI tools, and facilitating time-to-value.
• Stay abreast of industry trends, emerging technologies, and best practices in data science and facilitate knowledge sharing for capability building.

Key Requirements

• PhD or equivalent advanced qualifications in Computer Science, Statistics, Mathematics, Operations Research, or in fields related to AI/ML, data mining, deep learning, etc.
• Deep technical expertise in areas such as recommender systems, classification, optimization, deep learning, graph mining, anomaly detection, time-series forecasting, causal and statistical reasoning.
• Minimum 10 years of applied data science experience in industry (ideally banking, e-commerce, technology & telecommunications or equivalent) with proven track record of delivering business impact. Academic experience with track record of innovative research will be a plus.
• Rich experience working on large structured & unstructured datasets, and implementing large-scale data science solutions. Proven track record of deploying solutions into production, integrating them into workflows, and implementing continuous monitoring and enhancements. Experience in implementing large language models at scale will be a plus.
• Strong communication, interpersonal and stakeholder management skills, with an ability to present analysis and insights in a manner accessible by non-practitioners.
• Resourceful attitude with a ‘can-do’ spirit.

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